Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations91
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)3.3%
Total size in memory11.5 KiB
Average record size in memory129.4 B

Variable types

Numeric15
Categorical1

Alerts

Dataset has 3 (3.3%) duplicate rowsDuplicates
METABOLITE 0 is highly overall correlated with METABOLITE 1 and 13 other fieldsHigh correlation
METABOLITE 1 is highly overall correlated with METABOLITE 0 and 12 other fieldsHigh correlation
METABOLITE 10 is highly overall correlated with METABOLITE 0 and 11 other fieldsHigh correlation
METABOLITE 11 is highly overall correlated with METABOLITE 0 and 7 other fieldsHigh correlation
METABOLITE 12 is highly overall correlated with METABOLITE 0 and 13 other fieldsHigh correlation
METABOLITE 13 is highly overall correlated with METABOLITE 0 and 13 other fieldsHigh correlation
METABOLITE 14 is highly overall correlated with METABOLITE 0 and 9 other fieldsHigh correlation
METABOLITE 2 is highly overall correlated with METABOLITE 0 and 13 other fieldsHigh correlation
METABOLITE 3 is highly overall correlated with METABOLITE 0 and 13 other fieldsHigh correlation
METABOLITE 4 is highly overall correlated with METABOLITE 0 and 12 other fieldsHigh correlation
METABOLITE 5 is highly overall correlated with METABOLITE 0 and 13 other fieldsHigh correlation
METABOLITE 6 is highly overall correlated with METABOLITE 0 and 12 other fieldsHigh correlation
METABOLITE 7 is highly overall correlated with METABOLITE 0 and 13 other fieldsHigh correlation
METABOLITE 8 is highly overall correlated with METABOLITE 0 and 12 other fieldsHigh correlation
METABOLITE 9 is highly overall correlated with METABOLITE 0 and 11 other fieldsHigh correlation

Reproduction

Analysis started2024-10-12 09:22:02.975337
Analysis finished2024-10-12 09:22:32.246669
Duration29.27 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

METABOLITE 0
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89189414
Minimum-0.25570496
Maximum1.9769306
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)4.4%
Memory size856.0 B
2024-10-12T11:22:32.388325image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.25570496
5-th percentile0.051961532
Q10.46822473
median0.93283332
Q31.3073272
95-th percentile1.6797339
Maximum1.9769306
Range2.2326356
Interquartile range (IQR)0.83910247

Descriptive statistics

Standard deviation0.53683986
Coefficient of variation (CV)0.60190984
Kurtosis-0.90000741
Mean0.89189414
Median Absolute Deviation (MAD)0.41189736
Skewness-0.095334798
Sum81.162367
Variance0.28819704
MonotonicityNot monotonic
2024-10-12T11:22:32.563257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.452869757 2
 
2.2%
1.541666923 2
 
2.2%
1.344730673 2
 
2.2%
0.3226945007 1
 
1.1%
1.036556301 1
 
1.1%
1.272763963 1
 
1.1%
1.092197628 1
 
1.1%
1.35791165 1
 
1.1%
1.150105106 1
 
1.1%
1.277040159 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
-0.2557049628 1
1.1%
-0.190198383 1
1.1%
-0.02431565517 1
1.1%
-0.005725890767 1
1.1%
0.0480227937 1
1.1%
0.05590027107 1
1.1%
0.08698385202 1
1.1%
0.1510846284 1
1.1%
0.1575881282 1
1.1%
0.1579401511 1
1.1%
ValueCountFrequency (%)
1.976930594 1
1.1%
1.90745319 1
1.1%
1.893777335 1
1.1%
1.756200398 1
1.1%
1.687973836 1
1.1%
1.671493954 1
1.1%
1.648198178 1
1.1%
1.56711569 1
1.1%
1.55679231 1
1.1%
1.541666923 2
2.2%

METABOLITE 1
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0458714
Minimum-0.3417621
Maximum2.1977958
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.1%
Memory size856.0 B
2024-10-12T11:22:32.733369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.3417621
5-th percentile0.12499502
Q10.5109539
median1.0972481
Q31.5041225
95-th percentile1.9591424
Maximum2.1977958
Range2.5395579
Interquartile range (IQR)0.99316863

Descriptive statistics

Standard deviation0.59741817
Coefficient of variation (CV)0.57121572
Kurtosis-0.93558872
Mean1.0458714
Median Absolute Deviation (MAD)0.49542794
Skewness-0.068878216
Sum95.174295
Variance0.35690847
MonotonicityNot monotonic
2024-10-12T11:22:32.940829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.152910444 2
 
2.2%
1.746425942 2
 
2.2%
1.592675993 2
 
2.2%
0.5750239752 1
 
1.1%
1.421653749 1
 
1.1%
1.455111021 1
 
1.1%
1.21418804 1
 
1.1%
1.400487458 1
 
1.1%
1.472484947 1
 
1.1%
1.472788266 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
-0.3417621028 1
1.1%
0.06940702275 1
1.1%
0.07274310856 1
1.1%
0.11947325 1
1.1%
0.1227568153 1
1.1%
0.1272332308 1
1.1%
0.1670065525 1
1.1%
0.185687094 1
1.1%
0.2069927937 1
1.1%
0.2174829001 1
1.1%
ValueCountFrequency (%)
2.197795828 1
1.1%
2.117728792 1
1.1%
2.106060556 1
1.1%
2.044287273 1
1.1%
1.965646101 1
1.1%
1.952638778 1
1.1%
1.94113872 1
1.1%
1.898248878 1
1.1%
1.870308028 1
1.1%
1.746425942 2
2.2%

METABOLITE 2
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93037204
Minimum-0.27933275
Maximum2.7634306
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)3.3%
Memory size856.0 B
2024-10-12T11:22:33.143199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.27933275
5-th percentile0.079645141
Q10.42081759
median0.8666404
Q31.4211876
95-th percentile1.9518538
Maximum2.7634306
Range3.0427634
Interquartile range (IQR)1.00037

Descriptive statistics

Standard deviation0.62981087
Coefficient of variation (CV)0.67694519
Kurtosis-0.029928054
Mean0.93037204
Median Absolute Deviation (MAD)0.45958014
Skewness0.54867661
Sum84.663856
Variance0.39666174
MonotonicityNot monotonic
2024-10-12T11:22:33.324570image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6477345452 2
 
2.2%
0.9882383506 2
 
2.2%
0.8215676584 2
 
2.2%
0.7426445034 1
 
1.1%
2.184147999 1
 
1.1%
1.594229891 1
 
1.1%
1.709901387 1
 
1.1%
0.8548096577 1
 
1.1%
1.578141042 1
 
1.1%
1.431638415 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
-0.2793327468 1
1.1%
-0.05360927967 1
1.1%
-0.01770493663 1
1.1%
0.04451037679 1
1.1%
0.07756631884 1
1.1%
0.08172396222 1
1.1%
0.1035231807 1
1.1%
0.1341478979 1
1.1%
0.1845043475 1
1.1%
0.1939768149 1
1.1%
ValueCountFrequency (%)
2.763430641 1
1.1%
2.661106142 1
1.1%
2.184147999 1
1.1%
1.988808948 1
1.1%
1.96297205 1
1.1%
1.940735568 1
1.1%
1.919962761 1
1.1%
1.837392665 1
1.1%
1.77919319 1
1.1%
1.772577869 1
1.1%

METABOLITE 3
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87302585
Minimum-0.27237786
Maximum2.5082544
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)4.4%
Memory size856.0 B
2024-10-12T11:22:33.527390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.27237786
5-th percentile0.044595622
Q10.38704743
median0.80593674
Q31.2546626
95-th percentile1.8352624
Maximum2.5082544
Range2.7806322
Interquartile range (IQR)0.86761513

Descriptive statistics

Standard deviation0.60435833
Coefficient of variation (CV)0.69225708
Kurtosis-0.31508897
Mean0.87302585
Median Absolute Deviation (MAD)0.42883352
Skewness0.45192077
Sum79.445353
Variance0.36524899
MonotonicityNot monotonic
2024-10-12T11:22:33.742740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6224941696 2
 
2.2%
1.043107675 2
 
2.2%
0.7781044217 2
 
2.2%
0.6359577627 1
 
1.1%
2.016850715 1
 
1.1%
1.619218877 1
 
1.1%
1.678872138 1
 
1.1%
0.7909329594 1
 
1.1%
1.663690683 1
 
1.1%
1.293280695 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
-0.2723778626 1
1.1%
-0.08165375353 1
1.1%
-0.0352370608 1
1.1%
-0.01526739441 1
1.1%
0.03469926614 1
1.1%
0.05449197735 1
1.1%
0.05547580225 1
1.1%
0.07731338913 1
1.1%
0.0890224653 1
1.1%
0.1649084444 1
1.1%
ValueCountFrequency (%)
2.508254359 1
1.1%
2.474771602 1
1.1%
2.016850715 1
1.1%
1.911133183 1
1.1%
1.877674337 1
1.1%
1.79285046 1
1.1%
1.751904014 1
1.1%
1.742149065 1
1.1%
1.715012693 1
1.1%
1.678872138 1
1.1%

METABOLITE 4
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63170834
Minimum-0.19071126
Maximum1.7224313
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)6.6%
Memory size856.0 B
2024-10-12T11:22:33.950327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.19071126
5-th percentile-0.02168372
Q10.23590982
median0.61185886
Q30.9682365
95-th percentile1.3807057
Maximum1.7224313
Range1.9131425
Interquartile range (IQR)0.73232668

Descriptive statistics

Standard deviation0.45312704
Coefficient of variation (CV)0.71730418
Kurtosis-0.72848456
Mean0.63170834
Median Absolute Deviation (MAD)0.37386064
Skewness0.21071459
Sum57.485459
Variance0.20532411
MonotonicityNot monotonic
2024-10-12T11:22:34.198898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6480195654 2
 
2.2%
1.38070569 2
 
2.2%
0.9165013242 2
 
2.2%
0.5448036739 1
 
1.1%
1.18631994 1
 
1.1%
1.026045242 1
 
1.1%
0.9170929078 1
 
1.1%
0.4635566605 1
 
1.1%
1.263096719 1
 
1.1%
0.7530365204 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
-0.1907112596 1
1.1%
-0.1551917685 1
1.1%
-0.1239651933 1
1.1%
-0.100157222 1
1.1%
-0.02968606408 1
1.1%
-0.01368137579 1
1.1%
0.01443570706 1
1.1%
0.02394382645 1
1.1%
0.04222562737 1
1.1%
0.05211005847 1
1.1%
ValueCountFrequency (%)
1.722431251 1
1.1%
1.645549807 1
1.1%
1.526578485 1
1.1%
1.40913383 1
1.1%
1.38070569 2
2.2%
1.273750065 1
1.1%
1.265164501 1
1.1%
1.263096719 1
1.1%
1.208360529 1
1.1%
1.18631994 1
1.1%

METABOLITE 5
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0955732
Minimum-0.17163036
Maximum2.3576512
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.1%
Memory size856.0 B
2024-10-12T11:22:34.462051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.17163036
5-th percentile0.24259618
Q10.5158274
median1.1124538
Q31.5698445
95-th percentile2.0997364
Maximum2.3576512
Range2.5292816
Interquartile range (IQR)1.0540171

Descriptive statistics

Standard deviation0.61548477
Coefficient of variation (CV)0.56179247
Kurtosis-0.89088161
Mean1.0955732
Median Absolute Deviation (MAD)0.51162356
Skewness0.11397494
Sum99.69716
Variance0.3788215
MonotonicityNot monotonic
2024-10-12T11:22:34.702933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8267870995 2
 
2.2%
2.027793246 2
 
2.2%
1.701832695 2
 
2.2%
0.7853359478 1
 
1.1%
2.144022415 1
 
1.1%
2.105736133 1
 
1.1%
1.675606518 1
 
1.1%
0.8227335459 1
 
1.1%
1.429757043 1
 
1.1%
1.564167264 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
-0.1716303562 1
1.1%
0.04046792055 1
1.1%
0.06946784832 1
1.1%
0.1190003537 1
1.1%
0.2226970987 1
1.1%
0.2624952529 1
1.1%
0.2737582517 1
1.1%
0.2876179526 1
1.1%
0.3358239 1
1.1%
0.3371107751 1
1.1%
ValueCountFrequency (%)
2.357651229 1
1.1%
2.315483778 1
1.1%
2.205616919 1
1.1%
2.144022415 1
1.1%
2.105736133 1
1.1%
2.093736622 1
1.1%
2.054894281 1
1.1%
2.027793246 2
2.2%
2.019297203 1
1.1%
1.943677038 1
1.1%

METABOLITE 6
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.181257
Minimum-0.22123189
Maximum2.4434098
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)2.2%
Memory size856.0 B
2024-10-12T11:22:34.968189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.22123189
5-th percentile0.27235995
Q10.57986746
median1.1978242
Q31.6746094
95-th percentile2.2961536
Maximum2.4434098
Range2.6646417
Interquartile range (IQR)1.094742

Descriptive statistics

Standard deviation0.647798
Coefficient of variation (CV)0.54839719
Kurtosis-0.84026767
Mean1.181257
Median Absolute Deviation (MAD)0.5552753
Skewness0.028481343
Sum107.49438
Variance0.41964225
MonotonicityNot monotonic
2024-10-12T11:22:35.192243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.797439393 2
 
2.2%
1.800547932 2
 
2.2%
1.480287679 2
 
2.2%
0.8857560083 1
 
1.1%
2.352050548 1
 
1.1%
2.226297803 1
 
1.1%
1.818054131 1
 
1.1%
0.9405228149 1
 
1.1%
1.508979997 1
 
1.1%
1.963852307 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
-0.2212318942 1
1.1%
-0.03519676363 1
1.1%
0.09428807318 1
1.1%
0.1850556041 1
1.1%
0.2425155277 1
1.1%
0.3022043795 1
1.1%
0.3284212517 1
1.1%
0.3314995588 1
1.1%
0.3343489083 1
1.1%
0.340892059 1
1.1%
ValueCountFrequency (%)
2.443409836 1
1.1%
2.442424449 1
1.1%
2.423556319 1
1.1%
2.383756945 1
1.1%
2.352050548 1
1.1%
2.240256569 1
1.1%
2.226297803 1
1.1%
2.032128423 1
1.1%
2.021815077 1
1.1%
1.974420011 1
1.1%

METABOLITE 7
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75869126
Minimum-0.08133642
Maximum1.7802098
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)2.2%
Memory size856.0 B
2024-10-12T11:22:35.399933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.08133642
5-th percentile0.096696886
Q10.37529724
median0.71564314
Q31.1414602
95-th percentile1.4916603
Maximum1.7802098
Range1.8615462
Interquartile range (IQR)0.76616299

Descriptive statistics

Standard deviation0.45320694
Coefficient of variation (CV)0.59735357
Kurtosis-0.92119429
Mean0.75869126
Median Absolute Deviation (MAD)0.36215161
Skewness0.17876542
Sum69.040905
Variance0.20539653
MonotonicityNot monotonic
2024-10-12T11:22:35.583294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6952694334 2
 
2.2%
1.187991715 2
 
2.2%
0.738060303 2
 
2.2%
0.5779375849 1
 
1.1%
0.9836726231 1
 
1.1%
1.209056807 1
 
1.1%
1.314890293 1
 
1.1%
1.089066509 1
 
1.1%
1.361281265 1
 
1.1%
1.648490792 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
-0.08133642022 1
1.1%
-0.00524580344 1
1.1%
0.003278036331 1
1.1%
0.06623800745 1
1.1%
0.08982637708 1
1.1%
0.1035673955 1
1.1%
0.110286803 1
1.1%
0.1384182605 1
1.1%
0.1728822138 1
1.1%
0.1762394808 1
1.1%
ValueCountFrequency (%)
1.780209809 1
1.1%
1.648490792 1
1.1%
1.578275517 1
1.1%
1.564258985 1
1.1%
1.497260586 1
1.1%
1.486059917 1
1.1%
1.41357495 1
1.1%
1.401200877 1
1.1%
1.366384578 1
1.1%
1.361281265 1
1.1%

METABOLITE 8
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62442679
Minimum-0.22356176
Maximum1.2603071
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)2.2%
Memory size856.0 B
2024-10-12T11:22:35.763512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.22356176
5-th percentile0.14112385
Q10.38310003
median0.67090011
Q30.85032776
95-th percentile1.1022129
Maximum1.2603071
Range1.4838689
Interquartile range (IQR)0.46722773

Descriptive statistics

Standard deviation0.30871945
Coefficient of variation (CV)0.49440455
Kurtosis-0.30862662
Mean0.62442679
Median Absolute Deviation (MAD)0.22983589
Skewness-0.16568412
Sum56.822838
Variance0.095307698
MonotonicityNot monotonic
2024-10-12T11:22:36.023356image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8503277649 2
 
2.2%
1.219563792 2
 
2.2%
0.8927134309 2
 
2.2%
0.123016882 1
 
1.1%
1.101566016 1
 
1.1%
1.01239482 1
 
1.1%
0.4933096395 1
 
1.1%
0.8787777754 1
 
1.1%
0.9527431064 1
 
1.1%
0.7031613273 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
-0.223561757 1
1.1%
-0.08771576615 1
1.1%
0.07931222689 1
1.1%
0.123016882 1
1.1%
0.1274176614 1
1.1%
0.1548300331 1
1.1%
0.1665241093 1
1.1%
0.2386631908 1
1.1%
0.2409323215 1
1.1%
0.2602056146 1
1.1%
ValueCountFrequency (%)
1.260307123 1
1.1%
1.221233127 1
1.1%
1.219563792 2
2.2%
1.102859764 1
1.1%
1.101566016 1
1.1%
1.100666264 1
1.1%
1.01239482 1
1.1%
1.010333535 1
1.1%
0.9799745212 1
1.1%
0.9747052835 1
1.1%

METABOLITE 9
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2566371
Minimum0.30962058
Maximum3.0913266
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size856.0 B
2024-10-12T11:22:36.208809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.30962058
5-th percentile0.56242428
Q10.96973057
median1.2138643
Q31.5252404
95-th percentile1.9812751
Maximum3.0913266
Range2.781706
Interquartile range (IQR)0.55550981

Descriptive statistics

Standard deviation0.49578649
Coefficient of variation (CV)0.39453434
Kurtosis1.6195069
Mean1.2566371
Median Absolute Deviation (MAD)0.28734199
Skewness0.81253089
Sum114.35398
Variance0.24580425
MonotonicityNot monotonic
2024-10-12T11:22:36.411668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9104880547 2
 
2.2%
1.148468385 2
 
2.2%
1.956374199 2
 
2.2%
0.7016894266 1
 
1.1%
1.544974804 1
 
1.1%
1.546567092 1
 
1.1%
1.875282028 1
 
1.1%
1.327297554 1
 
1.1%
1.263655042 1
 
1.1%
1.481736821 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
0.3096205849 1
1.1%
0.3274178215 1
1.1%
0.4818671831 1
1.1%
0.5021733563 1
1.1%
0.5318009802 1
1.1%
0.5930475822 1
1.1%
0.6011337535 1
1.1%
0.6102619113 1
1.1%
0.6245954498 1
1.1%
0.652068492 1
1.1%
ValueCountFrequency (%)
3.091326603 1
1.1%
2.699550711 1
1.1%
2.424649291 1
1.1%
2.140717741 1
1.1%
2.006175958 1
1.1%
1.956374199 2
2.2%
1.894714541 1
1.1%
1.875282028 1
1.1%
1.856488721 1
1.1%
1.833443774 1
1.1%

METABOLITE 10
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2092098
Minimum0.2670208
Maximum3.0240879
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size856.0 B
2024-10-12T11:22:36.599318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.2670208
5-th percentile0.57522303
Q10.90504952
median1.1459547
Q31.4581146
95-th percentile1.957899
Maximum3.0240879
Range2.7570671
Interquartile range (IQR)0.55306505

Descriptive statistics

Standard deviation0.49193693
Coefficient of variation (CV)0.40682512
Kurtosis1.7297056
Mean1.2092098
Median Absolute Deviation (MAD)0.28604003
Skewness0.94971874
Sum110.03809
Variance0.24200194
MonotonicityNot monotonic
2024-10-12T11:22:36.829698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9148409446 2
 
2.2%
1.023534561 2
 
2.2%
1.821805169 2
 
2.2%
0.7312540017 1
 
1.1%
1.464580213 1
 
1.1%
1.527132942 1
 
1.1%
1.888541311 1
 
1.1%
1.356921158 1
 
1.1%
1.097508344 1
 
1.1%
1.349304416 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
0.2670207981 1
1.1%
0.3767050069 1
1.1%
0.4960677236 1
1.1%
0.5115854226 1
1.1%
0.5423732756 1
1.1%
0.6080727942 1
1.1%
0.6081129174 1
1.1%
0.6122490334 1
1.1%
0.6149037802 1
1.1%
0.6272109477 1
1.1%
ValueCountFrequency (%)
3.0240879 1
1.1%
2.731945283 1
1.1%
2.374954875 1
1.1%
2.068447447 1
1.1%
2.027256693 1
1.1%
1.888541311 1
1.1%
1.859882498 1
1.1%
1.849704851 1
1.1%
1.821805169 2
2.2%
1.802871521 1
1.1%

METABOLITE 11
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93243544
Minimum-0.088224226
Maximum2.329079
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)2.2%
Memory size856.0 B
2024-10-12T11:22:37.022100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.088224226
5-th percentile0.21729157
Q10.61485012
median0.86027155
Q31.241693
95-th percentile1.8022816
Maximum2.329079
Range2.4173032
Interquartile range (IQR)0.62684288

Descriptive statistics

Standard deviation0.47372771
Coefficient of variation (CV)0.50805416
Kurtosis0.032131979
Mean0.93243544
Median Absolute Deviation (MAD)0.31361492
Skewness0.3952031
Sum84.851625
Variance0.22441794
MonotonicityNot monotonic
2024-10-12T11:22:37.203168image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8050952 2
 
2.2%
0.8106419898 2
 
2.2%
1.009181813 2
 
2.2%
0.8469795714 1
 
1.1%
0.6574840594 1
 
1.1%
0.8409545812 1
 
1.1%
0.795654133 1
 
1.1%
1.906921238 1
 
1.1%
-0.07297774744 1
 
1.1%
0.1447927449 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
-0.08822422564 1
1.1%
-0.07297774744 1
1.1%
0.08689345527 1
1.1%
0.1447927449 1
1.1%
0.2007644546 1
1.1%
0.2338186857 1
1.1%
0.3583848143 1
1.1%
0.3763952801 1
1.1%
0.3879272998 1
1.1%
0.4234352486 1
1.1%
ValueCountFrequency (%)
2.329078991 1
1.1%
1.906921238 1
1.1%
1.853187114 1
1.1%
1.8050952 2
2.2%
1.799467982 1
1.1%
1.671022963 1
1.1%
1.642789191 1
1.1%
1.631707763 1
1.1%
1.614496219 1
1.1%
1.566407355 1
1.1%

METABOLITE 12
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0383611
Minimum0.12922127
Maximum4.4270479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size856.0 B
2024-10-12T11:22:37.416873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.12922127
5-th percentile0.43253562
Q10.87374291
median1.5903294
Q33.2198316
95-th percentile4.1107084
Maximum4.4270479
Range4.2978266
Interquartile range (IQR)2.3460887

Descriptive statistics

Standard deviation1.2935933
Coefficient of variation (CV)0.63462422
Kurtosis-1.305437
Mean2.0383611
Median Absolute Deviation (MAD)0.92212157
Skewness0.34553913
Sum185.49086
Variance1.6733837
MonotonicityNot monotonic
2024-10-12T11:22:37.643260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.156737451 2
 
2.2%
0.4652429119 2
 
2.2%
1.037174502 2
 
2.2%
3.551957424 1
 
1.1%
0.5911517922 1
 
1.1%
0.6782741967 1
 
1.1%
0.6775884015 1
 
1.1%
1.828020018 1
 
1.1%
0.219430742 1
 
1.1%
1.389473894 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
0.1292212727 1
1.1%
0.219430742 1
1.1%
0.3818458779 1
1.1%
0.4038764435 1
1.1%
0.4146417386 1
1.1%
0.4504294949 1
1.1%
0.4652429119 2
2.2%
0.5058673259 1
1.1%
0.530065227 1
1.1%
0.5911517922 1
1.1%
ValueCountFrequency (%)
4.427047886 1
1.1%
4.274653536 1
1.1%
4.267944173 1
1.1%
4.234261263 1
1.1%
4.117673595 1
1.1%
4.103743168 1
1.1%
4.080044389 1
1.1%
4.070067052 1
1.1%
3.971197161 1
1.1%
3.955108502 1
1.1%

METABOLITE 13
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0600471
Minimum0.013354043
Maximum4.3240015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size856.0 B
2024-10-12T11:22:37.888349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.013354043
5-th percentile0.37510673
Q10.84921733
median1.6527917
Q33.2821205
95-th percentile4.242204
Maximum4.3240015
Range4.3106475
Interquartile range (IQR)2.4329032

Descriptive statistics

Standard deviation1.3357199
Coefficient of variation (CV)0.6483929
Kurtosis-1.3470159
Mean2.0600471
Median Absolute Deviation (MAD)1.0252814
Skewness0.29510107
Sum187.46428
Variance1.7841476
MonotonicityNot monotonic
2024-10-12T11:22:38.135775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.152552766 2
 
2.2%
0.4508390168 2
 
2.2%
1.037139851 2
 
2.2%
3.74283179 1
 
1.1%
0.5205206693 1
 
1.1%
0.6042554415 1
 
1.1%
0.5786842961 1
 
1.1%
1.652791709 1
 
1.1%
0.01335404341 1
 
1.1%
1.489618477 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
0.01335404341 1
1.1%
0.08275708359 1
1.1%
0.3128119535 1
1.1%
0.3597897595 1
1.1%
0.3750904271 1
1.1%
0.375123032 1
1.1%
0.4508390168 2
2.2%
0.5013518912 1
1.1%
0.5205206693 1
1.1%
0.5407227075 1
1.1%
ValueCountFrequency (%)
4.324001509 1
1.1%
4.294464028 1
1.1%
4.281932066 1
1.1%
4.279279202 1
1.1%
4.273676468 1
1.1%
4.210731438 1
1.1%
4.06781263 1
1.1%
4.02548252 1
1.1%
4.023024185 1
1.1%
3.998375222 1
1.1%

METABOLITE 14
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0271468
Minimum0.11781349
Maximum2.2196735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size856.0 B
2024-10-12T11:22:38.316889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.11781349
5-th percentile0.35834258
Q10.66029079
median1.0569291
Q31.3197637
95-th percentile1.6576771
Maximum2.2196735
Range2.10186
Interquartile range (IQR)0.65947286

Descriptive statistics

Standard deviation0.42331597
Coefficient of variation (CV)0.41212801
Kurtosis-0.27566672
Mean1.0271468
Median Absolute Deviation (MAD)0.31288146
Skewness0.1582976
Sum93.470359
Variance0.17919641
MonotonicityNot monotonic
2024-10-12T11:22:38.518894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.081570539 2
 
2.2%
0.4081562018 2
 
2.2%
0.9303073769 2
 
2.2%
1.719948651 1
 
1.1%
0.5446058994 1
 
1.1%
0.7506146626 1
 
1.1%
0.7318527482 1
 
1.1%
0.5398880325 1
 
1.1%
0.3068583771 1
 
1.1%
0.9795895484 1
 
1.1%
Other values (78) 78
85.7%
ValueCountFrequency (%)
0.1178134885 1
1.1%
0.2250277419 1
1.1%
0.3033446822 1
1.1%
0.3068583771 1
1.1%
0.3085289525 1
1.1%
0.4081562018 2
2.2%
0.424662783 1
1.1%
0.4950829724 1
1.1%
0.4989354312 1
1.1%
0.5398880325 1
1.1%
ValueCountFrequency (%)
2.219673494 1
1.1%
1.939931371 1
1.1%
1.931235895 1
1.1%
1.719948651 1
1.1%
1.66721243 1
1.1%
1.648141719 1
1.1%
1.609521448 1
1.1%
1.58428774 1
1.1%
1.562883935 1
1.1%
1.562509755 1
1.1%

TYPE
Categorical

Distinct3
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size856.0 B
MENINGIOMA
52 
ASTROCYTOMA
23 
GLIOBLASTOMA
16 

Length

Max length12
Median length10
Mean length10.604396
Min length10

Characters and Unicode

Total characters965
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMENINGIOMA
2nd rowMENINGIOMA
3rd rowMENINGIOMA
4th rowMENINGIOMA
5th rowMENINGIOMA

Common Values

ValueCountFrequency (%)
MENINGIOMA 52
57.1%
ASTROCYTOMA 23
25.3%
GLIOBLASTOMA 16
 
17.6%

Length

2024-10-12T11:22:38.734816image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-12T11:22:38.952501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
meningioma 52
57.1%
astrocytoma 23
25.3%
glioblastoma 16
 
17.6%

Most occurring characters

ValueCountFrequency (%)
M 143
14.8%
O 130
13.5%
A 130
13.5%
I 120
12.4%
N 104
10.8%
G 68
7.0%
T 62
6.4%
E 52
 
5.4%
S 39
 
4.0%
L 32
 
3.3%
Other values (4) 85
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 965
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 143
14.8%
O 130
13.5%
A 130
13.5%
I 120
12.4%
N 104
10.8%
G 68
7.0%
T 62
6.4%
E 52
 
5.4%
S 39
 
4.0%
L 32
 
3.3%
Other values (4) 85
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 965
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 143
14.8%
O 130
13.5%
A 130
13.5%
I 120
12.4%
N 104
10.8%
G 68
7.0%
T 62
6.4%
E 52
 
5.4%
S 39
 
4.0%
L 32
 
3.3%
Other values (4) 85
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 965
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 143
14.8%
O 130
13.5%
A 130
13.5%
I 120
12.4%
N 104
10.8%
G 68
7.0%
T 62
6.4%
E 52
 
5.4%
S 39
 
4.0%
L 32
 
3.3%
Other values (4) 85
8.8%

Interactions

2024-10-12T11:22:29.772818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:03.288641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:05.041458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:06.804470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:08.604954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:10.573350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:12.564311image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:14.353615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:16.047551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:18.465339image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:20.694735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:22.473969image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:24.324184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:26.231170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:28.067638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:29.918511image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:03.398985image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:05.135340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:06.935824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:08.699287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:10.690634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:12.652277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:14.461531image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:16.178206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:18.622854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:20.782816image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:22.595141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:24.469599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:26.358711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:28.188308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:16.548094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:09.399845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:13.273400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:21.441748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:07.766860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:09.657036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:11.614885image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:13.521829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:15.271370image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:17.231884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:19.835170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:21.601040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:23.415050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:25.392544image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:27.280257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:29.000587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:30.971226image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:04.344294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:21.718082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:23.546621image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:25.571291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:27.431545image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:29.129293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:31.104139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:04.470412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:06.203904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:08.005027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:09.897669image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:11.919674image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:13.744817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:15.503600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:17.549525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:20.087944image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:21.820185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:23.663118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:25.668310image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:27.541920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:29.244830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:12.067536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:17.715230image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:20.205153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:23.795342image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:25.765843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:27.636531image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:29.370003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:31.355093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:04.726807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:06.444714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:08.256258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:12.180287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:13.998911image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:20.337621image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:22.072341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:23.950960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:25.846896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:29.471051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:12.320472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:14.126401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:20.449473image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:22.175464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-10-12T11:22:31.565521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:04.935249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:06.684277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:08.522884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:10.451223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:12.444567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:14.231993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:15.915081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:18.257527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:20.571841image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:22.315146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:24.204026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:26.098919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:27.979601image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-12T11:22:29.670781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-10-12T11:22:39.108847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
METABOLITE 0METABOLITE 1METABOLITE 10METABOLITE 11METABOLITE 12METABOLITE 13METABOLITE 14METABOLITE 2METABOLITE 3METABOLITE 4METABOLITE 5METABOLITE 6METABOLITE 7METABOLITE 8METABOLITE 9TYPE
METABOLITE 01.0000.8070.556-0.511-0.763-0.763-0.5470.7400.7240.7180.7190.7020.7070.6330.5370.383
METABOLITE 10.8071.0000.584-0.482-0.750-0.754-0.5480.8020.7980.8340.7920.7650.6720.6940.5540.392
METABOLITE 100.5560.5841.000-0.421-0.631-0.627-0.3890.6800.6780.5450.6500.6770.6570.6110.9790.225
METABOLITE 11-0.511-0.482-0.4211.0000.6890.6590.335-0.601-0.602-0.429-0.539-0.585-0.521-0.199-0.4100.374
METABOLITE 12-0.763-0.750-0.6310.6891.0000.9960.679-0.828-0.823-0.754-0.793-0.787-0.759-0.597-0.6110.402
METABOLITE 13-0.763-0.754-0.6270.6590.9961.0000.704-0.830-0.827-0.761-0.796-0.788-0.765-0.617-0.6070.436
METABOLITE 14-0.547-0.548-0.3890.3350.6790.7041.000-0.577-0.582-0.649-0.546-0.486-0.530-0.536-0.3420.453
METABOLITE 20.7400.8020.680-0.601-0.828-0.830-0.5771.0000.9910.8400.8260.8590.8720.6140.6560.414
METABOLITE 30.7240.7980.678-0.602-0.823-0.827-0.5820.9911.0000.8440.8280.8590.8690.6290.6560.402
METABOLITE 40.7180.8340.545-0.429-0.754-0.761-0.6490.8400.8441.0000.7990.7480.7360.7530.5200.442
METABOLITE 50.7190.7920.650-0.539-0.793-0.796-0.5460.8260.8280.7991.0000.9710.7820.7340.6450.403
METABOLITE 60.7020.7650.677-0.585-0.787-0.788-0.4860.8590.8590.7480.9711.0000.8050.6750.6670.384
METABOLITE 70.7070.6720.657-0.521-0.759-0.765-0.5300.8720.8690.7360.7820.8051.0000.6660.6450.374
METABOLITE 80.6330.6940.611-0.199-0.597-0.617-0.5360.6140.6290.7530.7340.6750.6661.0000.6030.419
METABOLITE 90.5370.5540.979-0.410-0.611-0.607-0.3420.6560.6560.5200.6450.6670.6450.6031.0000.275
TYPE0.3830.3920.2250.3740.4020.4360.4530.4140.4020.4420.4030.3840.3740.4190.2751.000

Missing values

2024-10-12T11:22:31.765241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-12T11:22:32.109285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

METABOLITE 0METABOLITE 1METABOLITE 2METABOLITE 3METABOLITE 4METABOLITE 5METABOLITE 6METABOLITE 7METABOLITE 8METABOLITE 9METABOLITE 10METABOLITE 11METABOLITE 12METABOLITE 13METABOLITE 14TYPE
00.3226950.5750240.7426450.6359580.5448040.7853360.8857560.5779380.1230170.7016890.7312540.8469803.5519573.7428321.719949MENINGIOMA
10.4574080.7647000.4444510.4513000.3882130.4015970.3825740.4076320.7150721.2160841.2044902.3290793.1793663.1173511.252479MENINGIOMA
21.4528701.1529100.6477350.6224940.6480200.8267870.7974390.6952690.8503280.9104880.9148411.8050952.1567372.1525531.081571MENINGIOMA
30.1575880.3144150.2516130.1864980.1005560.2876180.3343490.2138020.3058520.7540960.6272111.5664074.2746544.2107311.128131MENINGIOMA
40.3686810.2069930.2115210.0346990.1641870.3645900.3408920.1102870.2602060.5318010.4960680.6548054.0700674.3240021.350580MENINGIOMA
50.5349060.4962310.4208350.357735-0.1551920.3371110.5818460.287670-0.2235621.0352800.8952580.5810571.5903291.7075821.931236MENINGIOMA
60.7516630.4719450.4070600.3191560.1889340.3829460.5997450.5233320.5231291.0456861.0050610.7726322.7622562.9012361.461610MENINGIOMA
70.7062670.8892461.2288311.1223820.5777291.4344511.6563720.7506810.6482973.0913273.0240880.4824760.4504290.3597900.495083MENINGIOMA
81.9769311.1285071.1405271.0525870.8326321.3471691.4867341.0707850.7621201.8564891.8028720.2007641.4510151.5306931.199146MENINGIOMA
90.2336810.1272330.1939770.164908-0.0136810.3538540.5180210.1035670.3113691.1506191.0077411.3768073.2474723.2545911.667212MENINGIOMA
METABOLITE 0METABOLITE 1METABOLITE 2METABOLITE 3METABOLITE 4METABOLITE 5METABOLITE 6METABOLITE 7METABOLITE 8METABOLITE 9METABOLITE 10METABOLITE 11METABOLITE 12METABOLITE 13METABOLITE 14TYPE
811.5416671.7464260.9882381.0431081.3807062.0277931.8005481.1879921.2195641.1484681.0235350.8106420.4652430.4508390.408156GLIOBLASTOMA
820.8427051.0972480.9268400.9582060.9857191.1145401.1361190.9545781.1006661.1709801.0964840.6930640.7440200.6924610.663164GLIOBLASTOMA
831.1340581.7444700.9816560.7867500.7017110.9374211.1377970.8739360.5835602.0061762.0684470.8524841.2403501.3875851.088888GLIOBLASTOMA
841.5416671.7464260.9882381.0431081.3807062.0277931.8005481.1879921.2195641.1484681.0235350.8106420.4652430.4508390.408156GLIOBLASTOMA
851.8937771.8703080.6823880.7227370.7811101.3683021.2289470.6798901.2603071.4733701.3948820.9180501.4354241.2784390.571494GLIOBLASTOMA
861.1466211.9526391.2407001.2160440.8731401.3412801.3443960.5662930.7141662.1407182.0272570.7355640.9759081.0807360.984968GLIOBLASTOMA
871.3045851.0488781.4562521.3917730.7163651.2243941.4656541.3494100.8683882.6995512.7319450.7955050.7259810.7002570.622597GLIOBLASTOMA
881.6481981.9656460.9136880.9253841.0400361.3449631.3131330.6355870.9747051.4779501.4219180.6430541.2331301.2540601.052289GLIOBLASTOMA
890.5249460.7118190.4592940.4501230.5328910.4641260.5394420.4311460.2635411.0878151.0036630.8885942.2633182.3928482.219673GLIOBLASTOMA
901.1628672.1977960.9337190.9039780.8624332.3576512.4434100.5689441.0103341.0960591.0550410.9122580.8625380.7519150.636022GLIOBLASTOMA

Duplicate rows

Most frequently occurring

METABOLITE 0METABOLITE 1METABOLITE 2METABOLITE 3METABOLITE 4METABOLITE 5METABOLITE 6METABOLITE 7METABOLITE 8METABOLITE 9METABOLITE 10METABOLITE 11METABOLITE 12METABOLITE 13METABOLITE 14TYPE# duplicates
01.3447311.5926760.8215680.7781040.9165011.7018331.4802880.7380600.8927131.9563741.8218051.0091821.0371751.0371400.930307GLIOBLASTOMA2
11.4528701.1529100.6477350.6224940.6480200.8267870.7974390.6952690.8503280.9104880.9148411.8050952.1567372.1525531.081571MENINGIOMA2
21.5416671.7464260.9882381.0431081.3807062.0277931.8005481.1879921.2195641.1484681.0235350.8106420.4652430.4508390.408156GLIOBLASTOMA2